How Do Scientists Respond to Anomalies? Different Strategies Used in Basic and Applied Science

Abstract

We conducted two in vivo studies to explore how scientists respond to anomalies. Based on prior research, we identify three candidate strategies: mental simulation, mental manipulation of an image, and comparison between images. In Study 1, we compared experts in basic and applied domains (physics and meteorology). We found that the basic scientists used mental simulation to resolve an anomaly, whereas applied science practitioners mentally manipulated the image. In Study 2, we compared novice and expert meteorologists. We found that unlike experts, novices used comparison to address anomalies. We discuss the nature of expertise in the two kinds of science, the relationship between the type of science and the task performed, and the relationship of the strategies investigated to scientific creativity.

Relational Reasoning in Science, Medicine, and Engineering

“…An anomaly is any occurrence or observation that deviates from an expected pattern (Chinn and Brewer 1993;Trickett et al 2009). In this way, anomalous reasoning requires the understanding of a relational pattern that represents the norm in a given context, as well as the discernment of a discrepancy from that norm (Klahr and Dunbar 1988;Kulkarni and Simon 1988).…”

“…For example, if computer scientists are to be effective at debugging lines of code, they must understand how the commands within that code relate to one another, and then identify instances where the code deviates from those intended relations. Interestingly, anomalous reasoning has received the most scholarly attention from those within the literature on STEM thinking and learning (e.g., Darden 2006), connecting the construct to success in STEM domains such as engineering (Dumas and Schmidt 2015) and meteorology (Trickett et al 2009). However, empirical links to early reading skills have also been drawn by educational psychologists interested in anomalies more generally (Otero 2009).…”

“…Since Dunbar's studies, in vivo methods have been employed to tap relational reasoning in a variety of domains including engineering design (Chan and Schunn 2015), meteorology (Trickett et al 2009), and medicine (Dumas et al 2014), among others. For example, Chan and Schunn (2015) captured the analogical reasoning processes of a design team as they attempted to create a novel application of thermal printing technology.…”

Analogy as a strategy for supporting complex problem solving under uncertainty

“…Problem solvers across these settings draw on an array of domain-specific tools and methods to deal with uncertainty, such as rapid prototyping in engineering and design (Skelton & Thamhain, 2003) and statistical procedures such as Monte Carlo simulations and computational modeling in the sciences. Additionally, domain-general cognitive processes, such as mental simulation, appear to be important ways of dealing with uncertainty (Christensen & Schunn, 2009;Trickett, Trafton, & Schunn, 2009). As the range of domain-specific accounts, tools, and methods demonstrate, uncertainty is complex as a phenomenon and ubiquitous in complex real-world settings.…”

“…This psychological uncertainty can drive behavior such as decision making (Kahneman & Tversky, 1982) or problem solving to reduce uncertainty to acceptable levels (Trickett et al, 2009). If a problem solver is unaware of the uncertainty in available information, no psychological uncertainty exists, and no problem solving is required to resolve uncertainty.…”